Video practice systems and methods

ABSTRACT

A system and method may provide video content for training a user in an athletic motion or action. For example, video content may be provided with diminishing visibility to allow the user to visualize and imagine the action presented in the video content. In another example, a portion of a video content may be faded out, not displayed, or obscured to allow for visualization and imagination of the portion. In another example, video content may be presented in an manner that retains a user&#39;s interest despite repeated viewings.

CLAIM OF PRIORITY

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/523,476, filed on Jun. 22, 2017, the benefit ofpriority of which is claimed hereby, and which is incorporated byreference herein in its entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIGS. 1A-1C illustrate a series of video content displayed withdiminishing visibility in accordance with some examples,

FIG. 2 illustrates a flowchart showing a technique for virtualtransposition control in accordance with some examples.

FIG. 3 illustrates a flowchart showing a technique for virtualtransposition using partial obscuring in accordance with some examples.

FIG. 4 illustrates a flowchart showing a technique for user namepronunciation customization in accordance with some examples.

FIG. 5 illustrates a flowchart showing a technique for user nameinterjection in a video in accordance with some examples.

FIG. 6 illustrates a serialization graph for a Gaussian progressioncontrol system in accordance with some examples.

FIG. 7 illustrates a Random Number Generator (RNG) table in accordancewith some examples.

FIG. 8 illustrates a flowchart showing a technique for a customizablequick hitter video sequence in accordance with some examples.

FIG. 9 illustrates generally an example of a block diagram of a machineupon which any one or more of the techniques discussed herein mayperform in accordance with some embodiments.

DETAILED DESCRIPTION

Systems and methods described herein are used to provide video contentfor training a user in an athletic motion or action. For example, videocontent may be provided with diminishing visibility to allow the user tovisualize and imagine the action presented in the video content. Inanother example, a portion of a video content may be faded out, notdisplayed, or obscured to allow for visualization and imagination of theportion. In another example, video content may be presented in a mannerthat retains a user's interest despite repeated viewings.

Research shows that a person's brain may perceive two things from adistant conversation even if they are not paying attention to it. One ofthese is the gender of the speaker. The other is the sound of theperson's own name. In an example, a system includes a set of tools thatrecaptures user attention. Toward this end, the ability to produce thesound of the user's name is very valuable.

The systems and methods described herein include a platform of streamingvideo built around the concept of tracking user behavior to infer theirmental states. The systems and methods described herein may track anarray of data sources related to how the user interacts with thestreaming videos (pause rate, frequency of viewing, frequency ofstopping view mid sequence, number of views, duration of views, averageduration of stopped view, etc.) and combine this data to test forpatterns that indicate inattention. In doing so the system may have theability to stop a viewing mid-stream to switch to another video from thesystem's video library with the same movement skills content but with adifferent visual style. Determining that the user has become inattentivemay also be performed with biometric analysis.

The systems and methods described herein may add an additional elementto the video-switching mechanism by optionally including the sound ofthe user's name during the process of switching to a different visualstyle. In general, including the sound of the user's name may be used inconjunction with any attention-retaining mechanism to reinforce theattention-retaining mechanism. In an example, with the user namemechanism, the systems and methods described herein may choose to notchange the style and use the sound of the user's name to reacquireattention before returning to the same video content.

FIGS. 1A-1C illustrate a series of video content 104 displayed withdiminishing visibility in accordance with some examples. In FIG. 1A, thevideo content 104 is displayed fully opaque. The video content 104 maybe replayed at this full opacity a number of times before proceeding toa next visibility, or may be displayed just once. FIG. 1B shows thevideo content 104 at a first transparency level, for example 75% or 50%transparency. The video content 104 may further be displayed at a secondtransparency level (or third, or fourth, etc.), such as in aprogression. The progression from the opaque FIG. 1A presentation to thefirst transparency level (or from the first to later transparencylevels) may occur discretely (e.g., video content 104 presented opaquelyin a display and then the video content 104 presented at the firsttransparency level) or as gradually fading. For example, with the firsttransparency level at 75% (e.g., where 75% of the image is visible and25% of the background is shown through), one or more presentations ofthe video content 104 at 90%, 85%, etc., may be shown before the firsttransparency level. In another example, the video content 104 may fadeduring a single presentation (e.g., starting opaque and transitioning tothe first transparency level during a showing). FIG. 1C shows the videocontent 104 with a portion completely faded out (e.g., a model 106).Background content (e.g., visual or audio) may still be presented in thecompletely faded out scenario of FIG. 1C, but is shown blank in thefigure for simplicity.

The video content 104 may be presented to a user 102. The video content104 may feature a model 106, with a focal portion 108. In an example,the focal portion 108 may fade (e.g., gradual transition or jump to aparticular transparency or completely fade out) while the remainder ofthe video content 104 does not fade. For example, the focal portion 108may be in a first transparency level in FIG. 1B, while the remainder ofthe model 106 is still displayed in full opacity. The focal portion 108may be an arm, a leg, or another body part, or a portion of a body, aninstrument (e.g., a golf club or bat), or the like. Similarly, the focalportion 108 may be faded out entirely while the remainder of the model106 is still shown in full opacity, or at a level of transparency thatis less than 100%. By using a graduated transparency of the focalportion 108, the user 102 is provided “training wheels” for thevisualization of the imagery (e.g., model 106 and focal portion 108).The gradual reduction of opacity forces the user 102 to generate morethe imagery in their mind, as opposed to perceiving it directly, whichhelps train the user 102 to visualize more accurately.

In addition to video content 104, a speaker 110 (or other audiogeneration device) is used to provide audio cues to the user 102. Theaudio cues may be a tone to alert the user 102 of transitions in thevideo content 104.

FIG. 2 illustrates a flowchart showing a technique 200 for virtualtransposition control in accordance with some examples.

The technique 200 includes an operation 202 to display video content.

The technique 200 includes an operation 204 to gradually fade out colorvividness (e.g., display a portion of the video content at a firsttransparency level).

The technique 200 includes an operation 206 to optionally, while fading(or in between transparency levels), return to full vividness (e.g., ofa portion of the video content) to remind a user of the imagery intendedto be imagined.

The technique 200 includes an operation 208 to fade out a portion of thevideo content completely.

The technique 200 includes an operation 210 to optionally play an audiotone to alert the user. The audio tone may be used to alert the userthat video content is starting, is starting to fade, is done fading, iscompletely faded, that the user should close their eyes or open theireyes (e.g., at the beginning or end of an imagination period), that thefade out time is completed, or the like. Visual cues may be used toindicate any of these situations as well, except in conditions where theuser's eyes are closed. However, even in those conditions, a very brightvisual cue may still be effectively used.

The technique 200 includes an operation 212 to proceed to next videocontent. Operation 212 may include repeating the previously displayedvideo content (e.g., with different fade out timing or duration), ormoving onto a new video content (e.g., a second subskill related to afirst subskill displayed in the video content).

FIG. 3 illustrates a flowchart showing a technique 300 for virtualtransposition using partial obscuring in accordance with some examples.

The technique 300 includes an operation 302 to display video content.

The technique 300 includes an operation 304 to repeat the video content(e.g., with all or a portion of the video content at an increasedtransparency or remaining opaque).

The technique 300 includes an operation 306 to repeat the video contentwith a portion of the video content obscured (e.g., a body part or focalportion of the video content).

The technique 300 includes an operation 308 to optionally return to fullcontent (e.g., full opacity of any portions transparent or obscured) toremind a user of the imagery.

The technique 300 includes an operation 310 to proceed to a next videocontent. Operation 310 may include repeating the previously displayedvideo content (e.g., with different fade out timing or duration), ormoving onto a new video content (e.g., a second subskill related to afirst subskill displayed in the video content).

In an example, an Observational Learning (OL) based system may be usedto train a user. OL includes providing a user with an opportunity topassively take in imagery and learn as a result of the neural processingthat occurs to make sense of it. Mental Imagery Training (“MIT”) usesthe active building of imagery with a user's own imagination providing auser with an opportunity to see concepts in the mind's eye to stimulatelearning. Virtual Transposition (“VT”) is where these two concepts meet,providing the value of the control and detail level that OL may providewith the active nature of MIT which may itself create stronger encodingthan OL. In an example, it may be the case that the active nature of MITdoes not create stronger encoding, but the VT method benefits from asynergistic interplay between OL and MIT which creates the strongerencoding. The idea here is then the use of VT to create faster learningin a user. In an example, VT sequences may be built into a streamingvideo based training product.

Virtual Transposition includes showing imagery to a user and then askingthe user to replay that imagery in their own imagination. In an example,executing video demonstrations may be used with several repetitions ofcontent learned (e.g., a model, whether it be a human movement oranimation, etc.) and then providing an opportunity for the user toimagine the content, such as using a blank screen or instructions forthe viewer to close their eyes (or keep their eyes open but focus oninternal mental imagery). In an example, repetitions may be imagined ofthe exact imagery just observed. In an example, additional guidance maybe added to the process to ensure the active (imagined) part is as richand detailed as possible. Additional guidance may include visual,audible, or haptic feedback.

Instructions may be presented to a user on what is to happen and whattheir active part in it is. Several repetitions of a human movementskill may then be shown to the user. While showing those repetitions,the human movement skill imagery in the frame may gradually fade out(optionally the background may fade out or may remain vivid). The fadeout may include a portion of the human movement skill imagery, theentire imagery, or the entire video presented. While the imagery isgradually fading out, the user's task is to use their imagination toattempt to perceive it in their mind's eye in full vivid intensity andcolor. Eventually it may disappear completely, at which point the useris instructed to continue to imagine the previously demonstratedimagery. As discussed above, this process assists the user to create amore accurate visualization and as a result, obtain skills fasterthrough visualization.

This stage where the imagery has faded out entirely may be done with theeyes open the whole time which has the advantage of allowing the user toknow when to view new imagery since it may be displayed on screen. In anexample, the imagining may be done with the user's eyes closed towardthe end of the process. In this example, signal tones may be used toinstruct the user when the user's eyes are closed or when the user'seyes are open. For example, three signal tones may be used. The firsttone may chime just after the process of the image fading out begins.This is to tell the user that they may begin imagining the imagery infull vivid color as the screen fades. The second one may chime justbefore the imagery completely fades away and this may be to let the userknow to continue imagining the imagery (and optionally close theireyes). The second tone may instruct the user to continue imagining withthe eyes closed or open. The third tone may chime to let the user knowto proceed to a next video or process, or to open their eyes for thenext guided VT or to change to different VT or OL content.

In an example method extra vividness may be added to moments during thefading out process. The user may benefit from a brief reminder of thefull color vividness of the movement that the user is trying to rememberwith their imagination. For example, during the fade out process, a slowstrobe light effect may be introduced. The imagery may fade away, and beperiodically restored to full color and brightness for a brief timebefore returning to the fading process. An example set up may includethe strobe effect once every two seconds lasting for 0.1 seconds. Themovements shown may be done in slow motion. The strobe effect versionmay work better in slow motion than full speed, as slow motion allowsfor time to space out the bright and vivid instants of the strobeeffect.

In an example method, a full body (e.g., model) may be shown for severalrepetitions and then for a series of repetitions a display may show thesame imagery with a portion of the body obscured and ask the user tofill in the blank by imaging the imagery associated with the motion ofthe obscured part. During the portion where the user is using theirimagination to fill in the missing part, the user may be doing theimagining with their eyes open. In an example, images may be displayedas 3rd person or 1st person images. In an example, a duration of anoperation may be adjusted.

While performing any of these methods, executing an example version ofvirtual transposition, doing mental imagery training, or just doingobservational learning, the imagery may be either from a 3rd personperspective or a 1st person perspective. When imagining technique, a 3rdperson perspective may be used. In addition to imagining the details ofa technique a user may also imagine the outcome or results of atechnique. The imagining may be effective from a 1st person perspectivewhen internalizing the positive emotions of a good outcome or goodresults. In another example, from a 3rd person perspective the user maysee both technique and outcome or results and create an associationbetween the two. In an example, when the user is imagining the internalfeeling of executing a technique, it may be done from a 1st personperspective.

When executing VT, metrics may affect the learning value. These mayinclude duration of imagery displayed, duration of imagining theimagery, or ratio between the two. In an example, finding the rightduration leads to better retained attention and better vividness ofimagined imagery. More time viewing the displayed imagery may lead tobetter vividness during imagination, but may lead to inattention. Moretime imagining may lead to a greater learning result due to the activenature of mental imagery training, but the detail and vividness maydiminish over time. The best combination of the two may lead to theright ratio with the correct overall duration of a full cycle of VT. Inan example, a duration of displayed imagery may correspond to a durationof imagining the imagery e.g., imagining the imagery having a durationequal to the duration of displayed imagery, such as one presentation ofa movement).

FIG. 4 illustrates a flowchart showing a technique 400 for user namepronunciation customization in accordance with some examples.

The technique 400 includes an operation 402 to receive entry of a user'sname.

The technique 400 includes an operation 404 to present pronunciationoptions for the user's name.

The technique 400 includes an operation 406 to determine whether apronunciation presented has been selected by the user. If accepted, thetechnique 400 may end at operation 407.

If not accepted, the technique 400 includes an operation 408 to access apronunciation engine 409 used to improve user name pronunciation.

The pronunciation engine 409 is used to improve a pronunciation. Theimprovement may occur using speech sound options, a sound sequencingarray, syllable emphasis, a relative speed assignment graph, or a soundsmoothing feature. The technique 400 may apply one or more of thesefunctions to improve the user name pronunciation. After the applicationof the functions of the pronunciation engine 409, the technique returnsto operation 406 to determine if the user has accepted thepronunciation. If so, then the technique 400 ends at 407; if not, thenthe technique continues to operation 408 again. The technique 400 mayabort after a number of failed attempts to improve the pronunciation orin response to a user input.

FIG. 5 illustrates a flowchart showing a technique 500 for user nameinterjection in a video in accordance with some examples.

The technique 500 includes an operation 502 to detect user inattention(e.g., at a video display platform).

The technique 500 includes an operation 504 to generate an audiosequence featuring the user's name.

The technique 500 includes an operation 506 to interject the sequence(e.g., within the video content) including the user's name. In anexample, the technique 500 includes an operation 508 to prepare anintermission video including the user's name.

The technique 500 includes an operation 510 to, after the intermissionvideo is prepared, interject audio and the intermission video displayed(e.g., after original video content ends, or within a sequence of videocontent).

The technique 500 includes an operation 512 to play video content (e.g.,continue playing or play new video content), with the interjection audioplayed over the video content.

After the interjection finishes, new video content may be provided atoperation 514. In another example, after the interjection finishes, theoriginal video content may resume, such as with original audio, if any,at operation 516.

The technique 500 includes an operation 518 to reset inattentiondetection for the user (e.g., go back to the start of the technique 500and wait for another detection of user inattention).

In an example, it is possible to track biometric values (pupil dilation,heart rate, body temperature, etc.) measured from sensors monitoring theuser directly to detect user inattention. The purpose of tracking is todetermine a point where they exceed a threshold. Exceeding a thresholdthen triggers a content change in the video stream. Those operations arecovered elsewhere. This document is intended to discuss an optionaladditional technique that may be implemented when it is time to executea content change or to regain user attention without a content change.

The user may have their name attached to their user account. Whensetting up their profile they may be able to choose from severalpronunciations for their name so that the correct sound of their namemay be saved. If they may not find a satisfactory pronunciation, a soundprofile for a given name spelling via a customer service interaction maybe added.

Then, when a pattern of inattention is detected, an intervention may betriggered. In the case where User Name Interjection is used, thisintervention may use the following operations.

Stop the current video, which may include moving to other video imagerywhich is a black screen, white screen, color patterns, or other imagerythat is not relevant to movement skills training and add audio overlayon the fly.

An example of the audio overlay to add is the following, “[sound of username], we have detected that you are beginning to become less responsiveto the video content. We are going to change up the visual style for youto make things more interesting.”

Return to original video content or begin playing alternate videocontent.

Based on the way user data is tracked and analyzed, this trigger may bedetected in between viewings. In this case a delay may be built into theprocess. The delay may allow the user to watch the first five or sominutes of the next video and then implement the content switchsequence. In an example, the intervention may be executed at thebeginning of a video, but it is expected that it may have more impact ifit is implemented a few minutes into the video.

The time series choices related to movement skills information, salientcues, camera angles, and other video production choices may be optimizedin video production to produce the highest rates of improvement. Thatbeing the case, the system may use multiple versions of videos that havethe same time series sequencing as described in the previous sentence,but such that the different versions have different visual styles. Thenswitching between these videos which feature the same time seriessequencing of movement skills content involves stopping one at aspecific point in the time series from one video and then starting thenext video at that same time series position. The present discussion isabout interjecting the user's name in the way described above in betweenthe two videos during that switch. In an example, incorporating theuser's name into the audio overlay may be boldly done or in backgroundlike an Audio Jungle (a provider of background music for commercialvideo production) “audio watermark”. In an example, audio or visualinterjection of a name may be used where “visual interjection” meansadding a layer that displays the user's name in front of the normalvideo content to the video stream on the fly.

It may be the case that optimizing the exact time series in the videoproduction produces a limited effect. In that case, the exact sequenceof video production choices which may be strung together may be added toa list of things that may be varied to recapture user attention. Even inthat case, the user may be learning the same subject movement skillcontent, so that aspect may not change. However, in a more general case,even the specific subject matter may be changed to teach a user adifferent topic so long as the system may eventually return to completethe topic content that was switched away from when user inattention wasdetected.

Another possible use of the user name interjection is to add an audiosegment that lays over the top of the stream to get the user'sattention. This may be a loud or subtle addition of audio. Also, it mayreplace the audio of the video, making the standard audio go silent fora period and during that time the system may add in the audio of theuser's name.

In another embodiment the user name interjection may be multimodalmeaning that it would be displayed both with visuals and audio at thesame time.

FIG. 6 illustrates a serialization graph for a Gaussian progressioncontrol system in accordance with some examples.

A Gaussian varied practice assurance system may be used to generateprogressive video. In an example, the varied practice concept may beapplied to an automated coaching system.

The system quantifies user body position a user attempts to match amovement pattern that the system has demonstrated to them. In an examplesystem, the user may be guided toward a movement pattern that largelymatches the one that the system demonstrates. In an example, the systemmay progress to follow a refinement process while working on subsequenttechniques.

Varied practice has many benefits which may support both real andperceived value of utilizing the system. In an example, there is anotherconcept, the complex progression design and the serialization of saidprogression, uses an organized approach to sequencing training. In anexample skill acquisition scheme, progressions may exist at differentscales. In an example, there may be progressions within progressions. Inan example, any multidimensional array of discrete elements may beencoded into an ordered string. This is true for the full skillacquisition progression for a given discipline. In each case, thecomplex structure of a skill progression may be distilled into anordered string of exercises that the system may follow.

In an example, a rate of learning may increase by as much as double byimplementing varied practice. Also, a mechanism that drives these gainsis memory consolidation or encoding stimulation. When a stimulus is new,the rate of memory encoding that follows is far higher than when one haspreviously worked through a series of encounters with that stimulus.

In an example, a system may preserve a high rate of encoding. Thestimulus may be varied to ensure that the stimulus is changing. In anexample, the stimulus may be varied from say throwing a baseball pitch(the very thing to be improved) to kicking a soccer ball, the user maybe off track and no longer be encoding for the same task which may notbe desirable. In an example, when a new exercise is chosen, it may beclosely related to the task movement patterns to be encoded.

In short, a scheme that has a bunch of similar, but not too similar,exercises to work through where they are related to one “focus”technique may lead to the fastest learning. Further, this learning maybe more adaptable to dynamic situations as compared to learningresultant from a focus on one variation on a technique. This is morevaluable in a discipline like soccer where opponents may affect a user'stechnique greatly by forcing you to deal with their body mass andmomentum during play as opposed to say baseball pitching where they maynot influence you in that way.

Another large value-add related to varied practice is related to userexperience and the perception of enjoyment while using the system. Theconcept of boredom may be termed as a sensation of displeasure relatedto a lack of stimulus or a lack of novel stimulus.

Let's now address the serialization of the complex progression. Withinthe movement skill development world, the term “progression” is used todescribe the full scope of skill acquisition from basics to refinementat the highest levels of the activity. It also may refer to a series ofexercises to work on a single aspect of a technique. It is also appliedto slices of progression at scales in between those two. In an example,human coaches know the general progressions including the followingthree examples: the set of techniques that are to be acquired, drills tohelp with a technique, and details to address within a drill ortechnique which have some structure of “prerequisite” ordering.

In an example, the progression in a movement skill discipline is notserialized as a string of the smallest components, but is hierarchicallyorganized with sub progressions of drills under a technique, and subprogressions of details to get correct within a drill. Coaches thenimprovise to generally, but not precisely, follow a serializedprogression through the hierarchy.

In an example, a serialized progression through the hierarchy for adiscipline may be performed. That way it is clear for the system thatone exercise follows after another. An example system may includepredetermined standards of mastery for a skill. In an example, thesystem may be set up so that a user is to achieve a level of performancebefore advancing.

A serialized progression where you may not move from an exercise untilyou have reached a mastery or proficiency standard may eventually becomea “grind”. If, in an automated system, the user may get stuck on thesame exercise for an extended period of time as the user struggles toachieve mastery or proficiency, it may eventually result in eliminationof variety in the practice stimulus, boredom, and reduced encoding.

In an example, a system may allow manual intervention, allowing a user(possibly with the approval of an expert) to force the system to allowthem to work on a different task if they “get stuck”. That is generallyunsatisfactory because it implies that the user may get to a point offrustration with “getting stuck” (which would constitute an unpleasantuser experience) before they then choose to act. This Gaussian Overlaysystem is designed to preempt this possibility.

When an athlete is at a certain point in a progression, there may not bean actual tangible ability to determine where they are. Human coachesuse their expertise to guess where the athlete is in the progression, orto be more accurate, what the athlete is working on. Further, to keepthe athlete engaged, human coaches have players work on many exercises“around” their present threshold of “mastery” or “proficiency”. Finally,human coaches may also find supporting exercises that are similar to thecurrent area of focus that help to develop habits that bring about highquality movements when executing the primary exercises. These “supportexercises” may be considered to be part of the main progression eventhough they are not techniques that are intended to be used inperformance scenarios.

In order to replicate these factors in an automated system, a serializedprogression may be established for the system may work through. Thisallows us to organize which exercises may come generally prior toothers. Then, exercises may be selected to work on by assigningprobabilities to exercises in the area around the exercise at thecurrent threshold of proficiency and using a Random Number Generator(RNG) to choose a number which corresponds to the field which has beenassigned to an exercise. Then, the exercise that has been assigned thechosen number in their field may be the one that gets worked on.

“Work” on an exercise constitutes spending some amount of time learninghow to do it properly and executing repetitions. For example, five toten minutes. After this time, the progression may move forward if theconditions for doing so have been met. In another example, the processof assigning number fields to the exercises in consideration and usingan RNG to select a number may be repeated to select a new exercise. Itis possible that the most recent exercise (or most recent few) may beremoved from consideration for selection to ensure the randomizedselection process doesn't choose a long string of the same exercise,which may be a possibility however low the probability of occurring maybe.

Let us now consider some details of how this may be achieved. In anexample the RNG may be tasked with choosing a number between 1 and achosen number, “n”. For example, let us use a range between 1 and 1000.Probabilities may be assigned based on a distribution of probabilitiessuch that the full range between 1 and 1000 get used up. In anembodiment, the distribution may be modeled after a Gaussiandistribution.

Using a Gaussian distribution, allowing plus or minus four exercisesfrom the current threshold of proficiency, the system may center thedistribution on the exercise that is the threshold of proficiency andmay include values in the fields for exercises four posterior to thethreshold of proficiency and four anterior to the threshold ofproficiency. Then the classic bell curve of a Gaussian distribution maybe broken into nine parts with each containing an equal distance on thex-axis (not counting the 0.01% of the distribution that lies on theinfinite tails on either end of the Gaussian Distribution). Breaking itinto nine parts leaves a certain percentage of the total area under theGaussian distribution curve for the nine segments with the threesegments closest to the center containing the most.

To assign the field for an exercise, the percentage contained in theleft most segment of the distribution may be multiplied by 1000, roundedto the nearest whole number, and then assigned the numbers 1 to theresulting value to the most posterior exercise in the progression. Thepercentage of the segment to the right of this first one may besimilarly modified. After multiplying that percentage by 1000 androunding to the nearest whole number, that number of numbers to thefield for the corresponding exercise may be assigned such that thelowest numbers are chosen among those that are available after assigningthe first set of numbers to the first segment. This process may berepeated for all nine segments and the full field of numbers 1 to 1000may be assigned.

Once numbers are assigned, a RNG may be applied to select a number from1 to 1000 to choose which exercise to work on. FIG. 7 illustrates aRandom Number Generator (RNG) table in accordance with some examples.The RNG table of FIG. 7 may be used with some examples as describedherein.

In an example, this process may not need to be executed based on aGaussian Distribution. It may instead be executed on a triangulardistribution, a flat distribution, or even a customized distribution fora proficiency threshold exercise that correlates the exercise to itsmost closely related support exercises to make sure selection focuses onthose as opposed to merely somewhat related exercises.

However, the Gaussian distribution is built on a foundation that factorsin both a natural property of randomness and a tendency to clusteraround a central position. In an example, the distribution may becentered around a central point that is at least near to the currentthreshold of proficiency. True randomness around that point may be thebest distribution to foster the human perception of randomness and thuschanges throughout the progression and the Gaussian Distributionachieves those.

With the concept of selecting from a set of exercises that surrounds acentral exercise which is the user's current threshold of proficiency inplace, the system may deal with both progression and variety.Progression, in a basic sense, may operate the same way that it does ina strictly linear sequential progression system. Once a skill has beenpracticed to the point where a user may perform up to a certainstandard, that user may move on. In this case, moving on means theprogression moves forward down the line so the selection system has adifferent set of exercises to select from (if it moves forward oneexercise, then the set may be largely the same, but with differentprobabilities for the exercises and one new exercise replacing one oldexercise based on how the Gaussian Distribution overlays the exercisesequence).

Note that there is an interesting dynamic at play, however, in that thesystem may often be able to move forward multiple exercises. This isbecause there may be a large selection of exercises that lie ahead ofthe current threshold of proficiency where the user may becomeproficient in before they pass the standard of proficiency on theexercise where the threshold is. Then when they do achieve that standardthey may already have proficiency in one or more exercises ahead of thatone, meaning they may progress ahead multiple operations at that point.

Note also that there may be a trailing (or leading) threshold ofproficiency. This is a threshold that is centered a certain number ofexercises behind (or ahead) of the center of the distribution. Thethreshold being behind the center of the distribution may lead tosubstantial practice on an exercise before it becomes the threshold ofproficiency and minimal practice after the threshold is passed. This maybe executed by centering the distribution a few exercises ahead of thethreshold of proficiency and then doing the rest of the selectionprocess in the normal way. In an example, a tougher standard ofproficiency for a trailing threshold may be used.

Further, there may be multiple thresholds of proficiency in play. A goodexample may be to have a leading threshold which has a weaker standardof proficiency and a trailing threshold that has a tougher standard. Thedistribution may be positioned as determined by either standard suchthat if they do not agree it is positioned to the rearmost (relative tothe direction of the progression) location chosen between the twothresholds. Then, as the progression moves, the user may encounter apoint where an exercise becomes the leading threshold of proficiency andin order to progress they may have to achieve a low level ofproficiency. Then later as they progress that same exercise may becomethe trailing threshold of proficiency and they may achieve a high levelof proficiency on that same exercise in order to progress.

To further illustrate the idea of the rearmost position for thedistribution based on the two threshold tests, the user may pass theleading threshold test, but not the trailing. Then the leading thresholdmay move forward, but the distribution may not move forward until thetrailing threshold test was passed. Likewise, if they were able to passthe trailing threshold test, but not the leading one, the trailingthreshold may move forward, but the progression may not. Note that in anexample, the user may be made aware of the fact that they are facing aprogression test, or they may be tested during the normal course ofprogression (by keeping track of performance data). Also note that theremay be more than two threshold tests applied with effectively the samedynamic described above.

If the RNG takes an arbitrarily long time before selecting an exercisewhich is the current threshold exercise, then the user may not progressuntil it is selected and may not pass at that time either, forcing themto wait even longer. So, the system may apply a superseding rule thatsays, if the system hasn't selected the exercise that is the thresholdof proficiency for “n” iterations, then the next time it mayautomatically be chosen. This ensures that there may be regular andconsistent work on the exercise that the user may “test out on” in orderto progress. In the case where multiple thresholds are in play that areholding up progression, the system may choose a specific one by default.In an embodiment, the rearmost one (“trailing”) relative to thedirection of the progression may be chosen. In fact, it may be the casethat it is specifically not choosing this rearmost threshold whosestandard may be passed to progress which triggers this forced choice asopposed to considering between thresholds whose standard may be passed.

One may also consider how it may make sense to vary the nature of thedistribution as the user progresses. In the beginning fundamentals arefoundational. A less broad Gaussian distribution (one that considersfewer exercises in its selection range) is appropriate in this phase.Also, the system may likely allow fewer iterations that do not choosethe exercise that is the current threshold of proficiency before forcingthat choice. With progression, fundamentals are in place and morevariety in practice is likely of greater benefit, allowing for a broaderdistribution to be used.

In an example, changes may be made at the beginning or the end of theprogression. Progressions may be set up to include a finite number ofexercises. It is natural to label them as one may count them, startingat one and counting by one until the end of the progression. Then, adistribution centered on the first or the last exercise in theprogression sequence may wind up considering exercises which do notexist (e.g., hypothetical ones before the #1 exercise or after thehighest numbered exercise). In these cases, the distributions may runthe RNG such that if it chooses a number that does not correspond to anyexercise, it just runs the RNG again until it does choose one that isassigned. In an example, it may just reassign numbers for thenon-existent exercises in proper proportion to the ones that are in therange of the distribution and do have actual exercises assigned to them.

In many cases, a training progression is designed such that one exercisemay be introduced before another. This is dealt with in this GaussianDistribution Varied Practice Assurance System. In an example, a standardof proficiency test may be used to unlock the ability to be introducedto the dependent exercise.

One way to implement this may be to allow the dependent exercise to beselected, but if its pre-requisite (or pre-requisites) have not beensatisfied then the system defaults to the rearmost pre-requisiterelative to the distribution. In an example, any dependent exerciseswhose pre-requisites have not been chosen may have their selection fieldreassigned in some way to the pre-requisite exercises that the RNG maychoose from.

In an example, these contingencies may be non-issues if a pseudo-randomsequence of exercises was pre-arranged to be worked through whereadvancing still demands passing a skill proficiency threshold for eachexercise. This includes that an exercise in the serialized sequence mayhave a sub-sequence underneath it that approximates random selection ofit and the exercises around it to create a varied practice sequence towork through while the user tries to pass that exercise's proficiencystandard.

To do this, a randomization system of a type among those described abovemay be applied in advance such that surrounding exercises are consideredduring creation of the sub-sequence for each skill proficiency thresholdexercise. However, this randomization may be done during a productproduction phase as opposed to on the fly during user interaction as inthe cases described above. The result is a pre-fabricated sequence ofexercises (indeed a sub-sequence of the full progression for the sportor movement discipline) to work through while the user tries to pass theproficiency standard for a threshold for a certain exercise in a certainsport or other movement discipline. Also, considerations described aboveincluding establishing a pre-requisite system may be included in thedesign in this pre-fabricated method.

In an embodiment, the sequence of exercises may be long to ensure that arepresentative set of the possible exercises that may have the Gaussian(or other) distribution applied to them may be chosen. In this case, a“long” sequence may mean something like a string of 100 exercises. To bemore general, if the randomization system was considering nine exercisesto include in randomized order in the given sequence, then a longsequence may be ten times nine (ninety), or more. So, in general, it maybe around ten times the number of exercises in consideration at thatthreshold, or more.

After a first pass of randomizing a sequence in this way, a human orcomputer system may apply rules that rearrange the order of theexercises in the sequence to ensure:

no long string occurs with too many instances of the same exercises; and

each exercise is somewhat evenly distributed throughout the sequence inthe portions of the progression where that exercise fits. (Which meansthe result may not actually be random, but by starting with a randommethod and then applying subtle adjustments it may allow both of thefollowing qualities; the illusion of randomness for the user, goodrepresentation of exercises in all parts of the sequence).

The exercise that represents the current proficiency threshold may bechosen at least “n” number of selections.

In this way, a good logical selection of exercises may be appliedthroughout the progression. It also accommodates the case where the userpasses the threshold quickly by making sure that a good mix of exercisesis applied early in the sequence.

An additional consideration may be accounted for. What if the user doesnot pass the proficiency standard before the sequence is used up? Thesolution is just to restart the sequence and run it again until the userdoes pass. By making the sequence quite long, it is hard for the user todetect that they have worked through it and are starting over.

FIG. 8 illustrates a flowchart showing a technique 800 for acustomizable quick hitter video sequence in accordance with someexamples.

The technique 800 includes an operation 802 to receive user selection ofmusic, and optionally other settings, such as duration, tempo, or thelike.

The technique 800 includes an operation 804 to identify a time-seriesfor music qualities of the user selected music.

The technique 800 includes an operation 806 to map the time-series to avideo clip.

The technique 800 includes an operation 808 to assign the video clip toa portion of the time-series.

The technique 800 includes an operation 810 to trim the clip, slow theclip, expand a portion of the clip, repeat a portion of the clip or thelike to fit the time-series. The technique may return to operation 806to continue selecting and modifying video clips to fit the music.

The technique 800 includes an operation 812 to compile a video sequenceusing the music and any generated clips.

Many great businesses these days have achieved very rapid growth bycreating a system for users to create content and a platform for them toshare that content. With this, companies get two great benefits. Firstis growth via the efforts of customers to share with their friends whichmay be explosive. Second is user stickiness because they may not leavethe ecosystem without losing the network effect that becomes built intoit and the tools you create to keep that network engaging.

This stickiness factor may create limitations on one way to use anetwork effect to drive growth, that being sharing of user createdcontent, because it implies that they must use tools from inside theecosystem to create the user created content. Typically, this limitationis only a benefit to the company that hosts the social network however.

Another way to leverage a network effect is to make the value be in theconnections from user to user that it facilitates. This is the classicinternet social network. In this category, four types may be featured.

The social network where the connections from user to user are based onreal life friendships.

The entertainment social network where the choice to connect is based on“following” people and organizations that interest you or entertain you.These may be friends or celebrities.

The professional social network based on user perception that beingconnected to others may enhance their professional opportunities.

The geographic social network connecting people based on the proximityof their homes.

For some companies, opening the door to social and network effects mayrisk giving away the secret sauce. However, there may be asharing-of-user-created-content “light” concept which may reopen thedoor to network effects for a company like this. Within the visual-basedtraining paradigm, the user creation and sharing system may be used tocreate short customized videos. In addition to the satisfaction ofcreating something, users may find utility with these short customizedvideos mostly to prime the user's motor control circuitry prior to anactivity to get more out of practice or enhance in-game performance.

Customizable System

The following scheme outlines the mechanisms of user choice and thesystem for crafting customized outputs.

In order to create a customized system, users may go through thefollowing operations.

Choose or upload music—The system customization tool may allow the userto choose music from a list or to upload a music file in theirpossession.

Choose a technique—The user may then choose which technique that theymay like to make the subject of the system. Rules may be used for whattechnique may be made available to a user relative to their position intheir progression and as a way to limit how much secret sauce may beshared.

Additional Customization Options

The system may also accommodate adjustments related to the visual style

The system may adjust the editing style in terms of the timing of cuts,emphasis on slow motion, and repetition of same or similar clips

With those choices understood, it is useful to now consider whatsub-systems of the system they may modify.

A selection made with respect to customization types 1 and 3b above maymodify selection of techniques used to construct the system.

A selection made with respect to customization types 2 and 3a above maymodify the library from which clips are drawn from in order to fit theminto the output system.

The concept is to select clips from a library and then fit those into ascheme such that the nature of the music correlates to the nature of theclips and the timing of the cuts between clips. Let's now understand howthe system works. The first thing that may be done is to analyze thesong selection and classify its structure along its time line.

This structure contains things such as beats, harmonics, and movements.Certain video clips may be suited for certain types of harmonics ormovements. Clip lengths may be trimmed in order to fit such that cutsbetween clips may line up with the most intense beats in the music.

Once this structure is understood and a moment of the song is classifiedthose classifications may be matched up with metadata assigned to thevideo clips in the video library from which they may be chosen. This maybe done such that clips that are suited to certain musical structuresmay be arranged to match up to those times of the song that featurethose structures.

In order to match the cuts between clips to the most significant beatsof the song in a way that accommodates songs with timing between beatsthat may not be predicted in advance, the system may be capable ofadjusting the length of clips. There are a couple of ways to do this.One is to trim the clip. The other is to adjust the frame rate.

Trimming the clip is somewhat straight forward, except that a clip hassubject matter which is the focus of the imagery. In an example, thissubject matter is a human figure performing a technique. This subjectmatter may remain “centered” in the clip timing-wise. In an example, ifa clip is trimmed it may be trimmed equally from the beginning and theend of the clip. Metadata for the clip may include the timing of the“subject center” of the clip such that trimming be done symmetricallyaround that subject center and a minimum clip length that may be used toensure that the system does not trim it too short.

Adjustment of the frame rate of the clip may also have some rules.First, it is unlikely that the system may allow making a longer clipshorter by speeding up the frame rate of a clip to a “faster thannatural” speed. This is largely because the value of the Customizablesystem may be almost entirely in the user experience, and the experienceof watching things in fast motion tends to be unpleasant over sustainedperiods of time. If this assumption is not strictly true, there may atleast be a limit to how fast is allowed. Likewise, there may be limitsto how slow a clip may be made. Taking a non-slow motion clip and makingit slow involves duplicating frames. If this is taken too far the resultmay be unpleasant.

In an example, the library from which these clips may be selected may bemodified. Once again metadata assigned to a clip may provide a structurethrough which the field may be narrowed. So, in a sense the system hasits full library as a starting point. Then when the user chooses atechnique to be the subject of the video then clips featuring thattechnique may be under consideration. When a user chooses a visualstyle, the field may narrow further to include the selected techniquefeaturing the selected visual style. It is conceivable that the systemmay be configured to include multiple visual styles.

Multiple techniques may be trickier to accommodate. This is because theidentity of a system designed by a company for the purpose ofvisual-based movement skills training may need to be preserved and justmixing techniques for aesthetic reason gets too far away from thetraining effect focus upon which system technology is founded. So, ifthe user selects multiple techniques to include, the full duration ofthe song may be broken up in a special way to accommodate that choice.

The song may be broken up into n+1 parts (the exact timing of the switchfrom one part to the next may be on the beat which is nearest thecalculated cutoff point) where n is the number of techniques chosen.Then in the first part, technique 1 may be featured. In the second part,technique 2 may be featured. In part n, technique n may be featured.Then in the final portion of the song, the techniques may be mixedtogether in a “grand finale” sort of as you'd expect in a fireworksshow.

There may be rules for sharing though. These rules may be different ifyou are a subscriber to a service or if you are not. In order tofacilitate this, a free viewer app may be generated. This app mayconstruct the output video on the fly from a record of the audio inputand the sequence of clips selected. This stands opposed to making aself-contained “recording” of the output video to share. A sequence maythen be made available to non-subscribers for a certain period of timeor a certain number of viewings. Subscribers may have unlimited accessto shared sequences which are shared by their friends. In order toentice folks without even the viewer app, a select few self-containedvideos may be allowed, but this may be even more limited in order tolimit value to non-subscribers.

In examples of ways that sharing may be limited, if sharing is happeningto anyone outside of the group of subscribers to the system, theduration of the videos may be limited and the technique content choicesmay also be limited. In an example, when shared outside of thesubscriber community, the videos may be no longer than 30 seconds. In anexample, when shared outside of the subscriber community, the videos maybe limited to only a few techniques for each discipline. For example, inbaseball, maybe only videos based on a fastball pitch and a right handedbatting (for average) technique would be allowed to be shared outside ofthe subscriber community.

FIG. 9 illustrates generally an example of a block diagram of a machine900 upon which any one or more of the techniques discussed herein mayperform in accordance with some embodiments. In alternative embodiments,the machine 900 may operate as a standalone device or may be connected(e.g., networked) to other machines. In a networked deployment, themachine 900 may operate in the capacity of a server machine, a clientmachine, or both in server-client network environments. In an example,the machine 900 may act as a peer machine in peer-to-peer (P2P) (orother distributed) network environment. The machine 900 may be a virtualreality machine, a head mounted display, a personal computer (PC), atablet PC, a set-top box (SIB), a personal digital assistant (PDA), amobile telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein, such as cloudcomputing, software as a service (SaaS), other computer clusterconfigurations.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operationswhen operating. A module includes hardware. In an example, the hardwaremay be specifically configured to carry out a specific operation (e.g.,hardwired). In an example, the hardware may include configurableexecution units (e.g., transistors, circuits, etc.) and a computerreadable medium containing instructions, where the instructionsconfigure the execution units to carry out a specific operation when inoperation. The configuring may occur under the direction of theexecutions units or a loading mechanism. Accordingly, the executionunits are communicatively coupled to the computer readable medium whenthe device is operating. In this example, the execution units may be amember of more than one module. For example, under operation, theexecution units may be configured by a first set of instructions toimplement a first module at one point in time and reconfigured by asecond set of instructions to implement a second module.

Machine (e.g., computer system) 900 may include a hardware processor 902(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 904 and a static memory 906, some or all of which may communicatewith each other via an interlink (e.g., bus) 908. The machine 900 mayfurther include a display unit 910, an alphanumeric input device 912(e.g., a keyboard), and a user interface (UI) navigation device 914(e.g., a mouse). In an example, the display unit 910, alphanumeric inputdevice 912 and UI navigation device 914 may be a touch screen display.The machine 900 may additionally include a storage device (e.g., driveunit) 916, a signal generation device 918 (e.g., a speaker), a networkinterface device 920, and one or more sensors 921, such as a globalpositioning system (GPS) sensor, compass, accelerometer, or othersensor. The machine 900 may include an output controller 928, such as aserial (e.g., universal serial bus (USB), parallel, or other wired orwireless (e.g., infrared (IR), near field communication (NFC), etc.)connection to communicate or control one or more peripheral devices(e.g., a printer, card reader, etc.).

The storage device 916 may include a machine readable medium 922 that isnon-transitory on which is stored one or more sets of data structures orinstructions 924 (e.g., software) embodying or utilized by any one ormore of the techniques or functions described herein. The instructions924 may also reside, completely or at least partially, within the mainmemory 904, within static memory 906, or within the hardware processor902 during execution thereof by the machine 900. In an example, one orany combination of the hardware processor 902, the main memory 904, thestatic memory 906, or the storage device 916 may constitute machinereadable media.

While the machine readable medium 922 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) configured to store the one or moreinstructions 924.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 900 and that cause the machine 900 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. Specificexamples of machine readable media may include: non-volatile memory,such as semiconductor memory devices (e.g., Electrically ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM)) and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

The instructions 924 may further be transmitted or received over acommunications network 926 using a transmission medium via the networkinterface device 920 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), e Example communication networks may include a localarea network (LAN), a wide area network (WAN), a packet data network(e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 920 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 926. In an example, the network interfacedevice 920 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 900, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

Example 1 is a method comprising: receiving, at a processor of a userdevice, a selection of a technique including a physical motion to belearned by a user; displaying, at a display of the user device, videocontent of the technique performed by a model during a mental imagerytraining exercise; replaying the video content with a portion of themodel at a first transparency level; and in response to completion ofreplaying the video content, playing an audio tone after waiting aperiod of time, using a speaker of the user device to indicate themental imagery training exercise is complete.

In Example 2, the subject matter of Example 1 includes, replaying thevideo content with the portion of the model at a second transparencylevel after replaying the video content with the portion of the model atthe first transparency level, wherein the second transparency level ismore transparent than the first transparency level.

In Example 3, the subject matter of Example 2 includes, while replayingthe video content with the portion of the model at the secondtransparency level, replaying part of the video content at full opacity.

In Example 4, the subject matter of Examples 1-3 includes, replaying thevideo content with the portion of the model obscured, wherein theportion of the model is a body part performing an action correspondingto the mental imagery training exercise.

In Example 5, the subject matter of Example 4 includes, whereinreplaying the video content with the portion of the model obscuredincludes playing only a background of the video content.

In Example 6, the subject matter of Examples 1-5 includes, wherein theaudio tone is a second audio tone, and further comprising playing afirst audio tone when replaying the video content with the portion ofthe model at the first transparency level starts.

In Example 7, the subject matter of Examples 1-6 includes, wherein themodel is displayed using a third person perspective.

In Example 8, the subject matter of Examples 1-7 includes, tracking abiometric value of the user to detect whether the user is inattentive tothe video content, and in response to determining that the user isinattentive, playing audio including a name of the user.

In Example 9, the subject matter of Examples 1-8 includes, wherein themethod is repeated according to a serialized progression including aplurality of techniques similar to the technique based on the selectionof the technique.

In Example 10, the subject matter of Example 9 includes, wherein theplurality of techniques similar to the technique are selected based onprobabilities assigned to the plurality of techniques at a threshold ofproficiency of the user for the technique and a Random Number Generator.

Example 11 is a non-transitory machine-readable medium includinginstructions, which when executed by a processor cause the processor toperform operations to: receive, at a user device, a selection of atechnique including a physical motion to be learned by a user; outputfor display, at a display of the user device, video content of thetechnique performed by a model during a mental imagery trainingexercise; output for replaying the video content with a portion of themodel at a first transparency level; and in response to completion ofreplaying the video content, cause an audio tone to be played afterwaiting a period of time, using a speaker of the user device, toindicate the mental imagery training exercise is complete.

In Example 12, the subject matter of Example 11 includes, instructionsto cause the processor to output for replaying the video content withthe portion of the model at a second transparency level after replayingthe video content with the portion of the model at the firsttransparency level, wherein the second transparency level is moretransparent than the first transparency level.

In Example 13, the subject matter of Example 12 includes, instructionsto cause the processor to, while the video content with the portion ofthe model is replaying at the second transparency level, output forreplaying a part of the video content at full opacity.

In Example 14, the subject matter of Examples 11-13 includes,instructions to cause the processor to output for replaying the videocontent with the portion of the model obscured, wherein the portion ofthe model is a body part performing an action corresponding to themental imagery training exercise.

In Example 15, the subject matter of Example 14 includes, wherein tooutput for replaying the video content with the portion of the modelobscured, the instructions further cause the processor to output only abackground of the video content for playing.

Example 16 is a user device comprising: a processor coupled to memoryincluding instructions, which when executed by the processor, cause theprocessor to receive a selection of a technique including a physicalmotion to be learned by a user; a display to: display video content ofthe technique performed by a model during a mental imagery trainingexercise; and replay the video content with a portion of the model at afirst transparency level; and a speaker to, in response to completion ofreplaying the video content, play an audio tone after waiting a periodof time, the audio tone indicating the mental imagery training exerciseis complete.

In Example 17, the subject matter of Example 16 includes, wherein themodel is displayed using a third person perspective.

In Example 18, the subject matter of Examples 16-17 includes, abiometric component to track a biometric value of the user to detectwhether the user is inattentive to the video content, and wherein inresponse to determining that the user is inattentive, the speaker isfurther to play audio including a name of the user.

In Example 19, the subject matter of Examples 16-18 includes, whereinthe display is further to repeat the operations to display the videocontent, and replay the video content with the portion of the model atthe first transparency level, according to a serialized progressionincluding a plurality of techniques similar to the technique based onthe selection of the technique.

In Example 20, the subject matter of Example 19 includes, wherein theplurality of techniques similar to the technique are selected based onprobabilities assigned to the plurality of techniques at a threshold ofproficiency of the user for the technique and a Random Number Generator.

Example 21 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-20.

Example 22 is an apparatus comprising means to implement of any ofExamples 1-20.

Example 23 is a system to implement of any of Examples 1-20.

Example 24 is a method to implement of any of Examples 1-20.

Method examples described herein may be machine or computer-implementedat least in part. Some examples may include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods may include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code may include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code may be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

What is claimed is:
 1. A method comprising: receiving, at a processor ofa user device, a selection of a technique including a physical motion tobe learned by a user; displaying, at a display of the user device, videocontent of the technique performed by a model during a mental imagerytraining exercise; replaying the video content with a portion of themodel at a first transparency level; in response to completion ofreplaying the video content, playing an audio tone; after replaying thevideo content and after playing the audio tone, presenting a blankscreen for a period of time corresponding to a duration of the videocontent; and after expiration of the period of time, using a speaker ofthe user device to play the audio tone to indicate the mental imagerytraining exercise is complete.
 2. The method of claim 1, furthercomprising replaying the video content with the portion of the model ata second transparency level after replaying the video content with theportion of the model at the first transparency level, wherein the secondtransparency level is more transparent than the first transparencylevel.
 3. The method of claim 2, further comprising while replaying thevideo content with the portion of the model at the second transparencylevel, replaying part of the video content at full opacity.
 4. Themethod of claim 1, further comprising replaying the video content withthe portion of the model obscured, wherein the portion of the model is abody part performing an action corresponding to the mental imagerytraining exercise.
 5. The method of claim 4, wherein replaying the videocontent with the portion of the model obscured includes playing only abackground of the video content.
 6. The method of claim 1, wherein theaudio tone is a second audio tone, and further comprising playing afirst audio tone when replaying the video content with the portion ofthe model at the first transparency level starts.
 7. The method of claim1, wherein the model is displayed using a third person perspective. 8.The method of claim 1, further comprising tracking a biometric value ofthe user to detect whether the user is inattentive to the video content,and in response to determining that the user is inattentive, playingaudio including a name of the user.
 9. The method of claim 1, whereinthe method is repeated according to a serialized progression including aplurality of techniques similar to the technique based on the selectionof the technique.
 10. The method of claim 9, wherein the plurality oftechniques similar to the technique are selected based on probabilitiesassigned to the plurality of techniques at a threshold of proficiency ofthe user for the technique and a Random Number Generator.
 11. Anon-transitory machine-readable medium including instructions, whichwhen executed by a processor cause the processor to perform operationsto: receive, at a user device, a selection of a technique including aphysical motion to be learned by a user; output for display, at adisplay of the user device, video content of the technique performed bya model during a mental imagery training exercise; output for replayingthe video content with a portion of the model at a first transparencylevel; in response to completion of replaying the video content, causean audio tone to be played; after replaying the video content and afterplaying the audio tone, presenting a blank screen for a period of timecorresponding to a duration of the video content; and after expirationof the period of time, using a speaker of the user device, cause theaudio tone to be played to indicate the mental imagery training exerciseis complete.
 12. The machine-readable medium of claim 11, furthercomprising instructions to cause the processor to output for replayingthe video content with the portion of the model at a second transparencylevel after replaying the video content with the portion of the model atthe first transparency level, wherein the second transparency level ismore transparent than the first transparency level.
 13. Themachine-readable medium of claim 12, further comprising instructions tocause the processor to, while the video content with the portion of themodel is replaying at the second transparency level, output forreplaying a part of the video content at full opacity.
 14. Themachine-readable medium of claim 11, further comprising instructions tocause the processor to output for replaying the video content with theportion of the model obscured, wherein the portion of the model is abody part performing an action corresponding to the mental imagerytraining exercise.
 15. The machine-readable medium of claim 14, whereinto output for replaying the video content with the portion of the modelobscured, the instructions further cause the processor to output only abackground of the video content for playing.
 16. A user devicecomprising: a processor coupled to memory including instructions, whichwhen executed by the processor, cause the processor to receive aselection of a technique including a physical motion to be learned by auser; a display to: display video content of the technique performed bya model during a mental imagery training exercise; and replay the videocontent with a portion of the model at a first transparency level; and aspeaker to, in response to completion of replaying the video content,play an audio tone; wherein the display is further to, after replayingthe video content and after playing the audio tone, present a blankscreen for a period of time corresponding to a duration of the videocontent; and wherein after expiration of the period of time, the speakeris further to play the audio tone indicating the mental imagery trainingexercise is complete.
 17. The user device of claim 16, wherein the modelis displayed using a third person perspective.
 18. The user device ofclaim 16, further comprising a biometric component to track a biometricvalue of the user to detect whether the user is inattentive to the videocontent, and wherein in response to determining that the user isinattentive, the speaker is further to play audio including a name ofthe user.
 19. The user device of claim 16, wherein the display isfurther to repeat the operations to display the video content, andreplay the video content with the portion of the model at the firsttransparency level, according to a serialized progression including aplurality of techniques similar to the technique based on the selectionof the technique.
 20. The user device of claim 19, wherein the pluralityof techniques similar to the technique are selected based onprobabilities assigned to the plurality of techniques at a threshold ofproficiency of the user for the technique and a Random Number Generator.